Core Concept · Data Modeling

Medallion Architecture Explained: Bronze, Silver & Gold

The medallion architecture is a data design pattern that organizes lakehouse data into three progressively refined layers: Bronze holds raw ingested data, Silver holds cleaned and conformed data, and Gold holds business-ready aggregates. Data flows Bronze → Silver → Gold, getting more valuable and trustworthy at each step.

Last updated July 2026.

The three layers

Bronze · Raw

Land the data exactly as it arrives

Bronze is the raw landing zone. You ingest source data — files, streams, database change feeds — with as little transformation as possible, keeping a faithful, append-only history of what came in.

  • Raw, as-is; nothing dropped
  • Adds ingestion metadata (source file, load timestamp)
  • Reprocessable — you can always rebuild Silver and Gold from it
Silver · Cleaned

Clean, validate, and conform

Silver takes Bronze and makes it trustworthy: correct data types, deduplication, null handling, quality checks, and joins that resolve entities into a consistent, queryable model.

  • Typed, deduplicated, validated
  • Joined and conformed into clean entities
  • The "single source of truth" most analysts and pipelines read from
Gold · Business-ready

Aggregate for the business

Gold is purpose-built for consumption: aggregated metrics, dimensional models, and feature tables shaped for specific BI dashboards, reports, or ML use cases.

  • Business-level aggregates and KPIs
  • Optimized for read performance
  • What dashboards, reports, and models actually query

Why organize data this way?

Splitting the pipeline into layers gives you separation of concerns. Each stage has one job, which makes the whole system easier to debug, test, and trust:

Medallion architecture on Databricks

On the Databricks lakehouse, each layer is typically a set of Delta Lake tables governed by Unity Catalog. Raw data lands in Bronze via COPY INTO or Auto Loader; declarative pipelines and Spark SQL/PySpark transform Bronze into Silver and Silver into Gold, with expectations enforcing data quality along the way. Because every layer is a Delta table, you get ACID transactions, time travel, and schema enforcement at each step.

Exam tip: Watch for questions that describe a table's role rather than naming it. "Raw as-landed" = Bronze, "validated and conformed" = Silver, "business-level aggregates" = Gold. Recognizing the role is often the whole question.

FAQ

Is the medallion architecture required on Databricks?

No — it's a recommended pattern, not a hard rule. It's widely adopted because it makes pipelines easier to reason about, but you can use fewer or more layers depending on your needs.

What's the difference between Bronze and raw data?

They're essentially the same idea. "Bronze" is the medallion term for the raw landing layer — data ingested as-is, usually with a little ingestion metadata added, before any cleaning.

Can I have more than three layers?

Yes. Bronze/Silver/Gold is the standard three, but teams sometimes add sub-layers (for example, a raw vs. cleansed split within Silver). The principle — progressive refinement — matters more than the exact count.

Which layer do BI tools and dashboards read from?

Usually Gold, because it holds the business-ready aggregates shaped for reporting. Some ad-hoc analysis reads from Silver, which is clean and conformed but not yet aggregated.

Go deeper in the course

The Bronze → Silver → Gold flow is one of the most-tested ideas on the exam. Work through it hands-on in the transformation chapters.

Open the Medallion chapter →